Junior Data Scientist - Sales & Marketing

Junior Data Scientist - Sales & Marketing Hybrid in London, United Kingdom

We’re looking for a Junior Data Scientist

You'll design experiments and build the statistical models that shape how the company reaches new customers. You'll practise mastery of your craft, mentored by the team's data scientist, with the company's wider data science community around you.

Small businesses move fast. Opportunities often don’t wait, and cash flow pressures can appear overnight. To keep going, and growing, SMEs need finance that’s as flexible and responsive as they are.

That's why we built the company. Our smart technology, data science and five-star customer service ensures business owners can act with the speed, confidence and control they need, exactly when it's needed.

We’ve already cleared the way for 100,000 businesses with more than £4 billion in funding. Our passionate team is driven to help even more SMEs succeed, through access to better finance and other services that make running a business easier. Our ultimate mission is to support one million SMEs in their defining moments, creating lasting impact for the communities and economies they drive.

The Sales & Marketing team, also known as Direct Acquisition, owns how the company reaches UK small businesses – across paid and organic marketing channels and direct sales.

The team is autonomous and cross-functional, bringing together Marketing, Sales, Brand, and Acqui Tech – the company's acquisition technology team, which includes software engineering, product, design, and data science. As a Data Scientist, you'll work with people across each function.

As a Data Scientist in the Direct Acquisition team, you'll design experiments and build the statistical models that shape how the company reaches its next customers. Your findings feed directly into how we invest in sales and marketing.

You'll work alongside the team's data scientist, who mentors you closely as you take on more ownership.

• Experimentation. You'll design and evaluate experiments so decisions on sales and marketing rest on evidence.

• Modelling. You'll build models that solve the team's business problems, with input on approach and solution design.

• Community and mastery. You'll join the company's wider data science community – analysts, data scientists, and statisticians – to align on methodology and develop your expertise across acquisition.

• Statistical foundations. You have a background in probability and statistics from a quantitative field – typically a degree (or recent/final-year Master's) in Statistics, Mathematics, Physics, or similar. You reason about uncertainty and calibration as first-order concerns.

• Research mindset. You're curious about new methods and actively look for better ways to do things. You try them out, not just read about them.

• Judgement. You critically evaluate model output – yours, a colleague's, or an LLM's – and can explain why a choice is right. You defend your reasoning under challenge, and challenge others' when the evidence points elsewhere.

• Analytical ownership. You take work streams from framing to a landed decision, with support on solution design where you need it. You like to move fast, iterate, and update on new evidence rather than chase perfection.

• AI fluency. You use AI as a primary tool. You prototype with it, automate with it, and use judgement on where it helps and where it doesn't.

• Communication. You write and speak clearly, directly, and concisely. You adapt technical detail for non-technical colleagues in sales and marketing.

• Domain experience. You have worked on marketing, sales, or customer acquisition problems.

• Causal inference and experimentation. You have designed or analysed experiments where distinguishing signal from noise mattered – A/B tests, quasi-experiments, or lift studies.

• Marketing mix or attribution modelling. You have used or contributed to models that quantify channel impact or optimise spend.

• Bayesian methods. You have used hierarchical models, MCMC, or Bayesian updating in real work.

• Production ML. You have built and shipped supervised ML models end to end – exploration, training,…